Regression models for near-infrared measurement of subcutaneous adipose tissue thickness

Yu Wang, Dongmei Hao, Jingbin Shi, Zeqiang Yang, Lui JIn, Song Zhang, Yimin Yang, Guangyu Bin, Yanjun Zeng, Dingchang Zheng

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Abstract

Obesity is often associated with the risks of diabetes and cardiovascular disease, and there is a need to measure subcutaneous adipose tissue (SAT) thickness for acquiring the distribution of body fat. The present study aimed to develop and evaluate different model-based methods for SAT thickness measurement using an SATmeter developed in our laboratory. Near-infrared signals backscattered from the body surfaces from 40 subjects at 20 body sites each were recorded. Linear regression (LR) and support vector regression (SVR) models were established to predict SAT thickness on different body sites. The measurement accuracy was evaluated by ultrasound, and compared with results from a mechanical skinfold caliper (MSC) and a body composition balance monitor (BCBM). The results showed that both LR- and SVR-based measurement produced better accuracy than MSC and BCBM. It was also concluded that by using regression models specifically designed for certain parts of human body, higher measurement accuracy could be achieved than using a general model for the whole body. Our results demonstrated that the SATmeter is a feasible method, which can be applied at home and in the community due to its portability and convenience.
Original languageEnglish
Article number1024
JournalPhysiological Measurement
Volume37
Issue number7
DOIs
Publication statusPublished - 31 May 2016
Externally publishedYes

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This is an author-created, un-copyedited version of an article accepted for publication/published in Physiological Measurement, IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at 10.1088/0967-3334/37/7/1024

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